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001450290 001__ 1450290
001450290 003__ OCoLC
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001450290 019__ $$a1347223384
001450290 020__ $$a9783031179761$$q(electronic bk.)
001450290 020__ $$a3031179765$$q(electronic bk.)
001450290 020__ $$z9783031179754
001450290 020__ $$z3031179757
001450290 0247_ $$a10.1007/978-3-031-17976-1$$2doi
001450290 035__ $$aSP(OCoLC)1347378786
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001450290 049__ $$aISEA
001450290 050_4 $$aRC78.7.D53
001450290 08204 $$a616.07/57$$223/eng/20221013
001450290 1112_ $$aiMIMIC (Workshop)$$n(5th :$$d2022 :$$cSingapore).
001450290 24510 $$aInterpretability of machine intelligence in medical image computing :$$b5th international workshop, iMIMIC 2022, held in conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, proceedings /$$cMauricio Reyes, Pedro Henriques Abreu, Jaime Cardoso (eds.).
001450290 24630 $$aiMIMIC 2022
001450290 264_1 $$aCham :$$bSpringer,$$c[2022]
001450290 264_4 $$c©2022
001450290 300__ $$a1 online resource (x, 125 pages) :$$billustrations (chiefly color).
001450290 336__ $$atext$$btxt$$2rdacontent
001450290 337__ $$acomputer$$bc$$2rdamedia
001450290 338__ $$aonline resource$$bcr$$2rdacarrier
001450290 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v13611
001450290 500__ $$aInternational conference proceedings.
001450290 500__ $$aIncludes author index.
001450290 5050_ $$aIntro -- Preface -- Organization -- Contents -- Interpretable Lung Cancer Diagnosis with Nodule Attribute Guidance and Online Model Debugging -- 1 Introduction -- 2 Materials -- 3 Methodology -- 3.1 Collaborative Model Architecture with Attribute-Guidance -- 3.2 Debugging Model with Semantic Interpretation -- 3.3 Explanation by Attribute-Based Nodule Retrieval -- 4 Experiments and Results -- 4.1 Implementation -- 4.2 Quantitative Evaluation -- 4.3 Trustworthiness Check and Interpretable Diagnosis -- 5 Conclusions -- References
001450290 5058_ $$aDo Pre-processing and Augmentation Help Explainability? A Multi-seed Analysis for Brain Age Estimation -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Results -- 4.1 Performance -- 4.2 Voxel Agreement -- 4.3 Atlas-Based Analyses -- 4.4 Region Validation -- 5 Conclusion -- References -- Towards Self-explainable Transformers for Cell Classification in Flow Cytometry Data -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Architecture -- 3.2 Preprocessing -- 3.3 Loss Function -- 3.4 Data Augmentation -- 4 Experiments -- 4.1 Data -- 4.2 Results -- 5 Conclusion -- References
001450290 5058_ $$aReducing Annotation Need in Self-explanatory Models for Lung Nodule Diagnosis -- 1 Introduction -- 2 Method -- 3 Experimental Results -- 3.1 Prediction Performance of Nodule Attributes and Malignancy -- 3.2 Analysis of Extracted Features in Learned Space -- 3.3 Ablation Study -- 4 Conclusion -- References -- Attention-Based Interpretable Regression of Gene Expression in Histology -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 Multiple Instance Regression of Gene Expression -- 2.3 Attention-Based Model Interpretability -- 2.4 Evaluation of Performance and Interpretability
001450290 5058_ $$a3 Experiments and Results -- 3.1 Network Training -- 3.2 Quantitative Model Evaluation -- 3.3 Attention-Based Identification of Hotspots and Patterns -- 3.4 Quantitative Evaluation of the Attention -- 4 Discussion -- 5 Conclusion -- A Description of Selected Genes -- B Detailed Model Evaluation -- C Additional Visualizations -- D Single-Cell Co-expression -- References -- Beyond Voxel Prediction Uncertainty: Identifying Brain Lesions You Can Trust -- 1 Introduction -- 2 Our Framework: Graph Modelization for Lesion Uncertainty Quantification
001450290 5058_ $$a2.1 Monte Carlo Dropout Model and Voxel-Wise Uncertainty -- 2.2 Graph Dataset Generation -- 2.3 GCNN Architecture and Training -- 3 Material and Method -- 3.1 Data -- 3.2 Comparison with Known Approaches -- 3.3 Evaluation Setting -- 3.4 Implementation Details -- 4 Results and Discussion -- 5 Conclusion -- References -- Interpretable Vertebral Fracture Diagnosis -- 1 Introduction -- 1.1 Related Work -- 2 Methodology -- 2.1 Vertebral Fracture Detection -- 2.2 Semantic Concept Extraction (Correlation) -- 2.3 Visualization of Highly Correlating Concepts at Inference -- 3 Experimental Setup
001450290 506__ $$aAccess limited to authorized users.
001450290 520__ $$aThis book constitutes the refereed joint proceedings of the 5th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2022, held in September 2022, in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022. The 10 full papers presented at iMIMIC 2022 were carefully reviewed and selected from 24 submissions each. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. .
001450290 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 13, 2022).
001450290 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
001450290 650_0 $$aComputer-assisted surgery$$vCongresses.
001450290 655_0 $$aElectronic books.
001450290 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001450290 655_7 $$aConference papers and proceedings.$$2lcgft
001450290 7001_ $$aReyes, Mauricio,$$eeditor.
001450290 7001_ $$aHenriques Abreu, Pedro,$$eeditor.
001450290 7001_ $$aCardoso, Jaime S.$$q(Jaime dos Santos),$$eeditor.
001450290 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(25th :$$d2022 :$$cSingapore)
001450290 77608 $$iPrint version: $$z3031179757$$z9783031179754$$w(OCoLC)1342984213
001450290 830_0 $$aLecture notes in computer science ;$$v13611.$$x1611-3349
001450290 852__ $$bebk
001450290 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-17976-1$$zOnline Access$$91397441.1
001450290 909CO $$ooai:library.usi.edu:1450290$$pGLOBAL_SET
001450290 980__ $$aBIB
001450290 980__ $$aEBOOK
001450290 982__ $$aEbook
001450290 983__ $$aOnline
001450290 994__ $$a92$$bISE